Overview

Dataset statistics

Number of variables11
Number of observations250
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.6 KiB
Average record size in memory88.5 B

Variable types

NUM11

Reproduction

Analysis started2020-08-25 00:56:42.790995
Analysis finished2020-08-25 00:56:59.536920
Duration16.75 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
oz6 has unique values Unique
oz7 has unique values Unique
oz8 has unique values Unique
oz9 has unique values Unique
oz10 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.370246410369874e-10
Minimum-2.492960214614868
Maximum2.30313491821289
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:56:59.585390image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.492960215
5-th percentile-1.658052641
Q1-0.7612414211
median-0.01447139727
Q30.7701664418
95-th percentile1.618271697
Maximum2.303134918
Range4.796095133
Interquartile range (IQR)1.531407863

Descriptive statistics

Standard deviation1.000000003
Coefficient of variation (CV)1569797993
Kurtosis-0.6046433564
Mean6.37024641e-10
Median Absolute Deviation (MAD)0.7691705525
Skewness-0.06254409968
Sum1.592561603e-07
Variance1.000000006
2020-08-25T00:56:59.681040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.0440977923610.4%
 
0.693527698510.4%
 
0.281116157810.4%
 
1.13994169210.4%
 
0.401993155510.4%
 
-0.848005533210.4%
 
-2.49296021510.4%
 
-1.97325134310.4%
 
-0.695989191510.4%
 
-2.07124495510.4%
 
-1.75848746310.4%
 
0.913390874910.4%
 
-1.38148295910.4%
 
2.0514748110.4%
 
0.926930606410.4%
 
-1.12337994610.4%
 
1.10087192110.4%
 
-0.00922652892810.4%
 
-0.051376964910.4%
 
-1.16829502610.4%
 
-0.482769429710.4%
 
-1.74055528610.4%
 
0.010233474910.4%
 
-0.669100880610.4%
 
-0.59340083610.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-2.49296021510.4%
 
-2.35240602510.4%
 
-2.08433437310.4%
 
-2.07124495510.4%
 
-2.00507640810.4%
 
-1.97325134310.4%
 
-1.89115965410.4%
 
-1.88648080810.4%
 
-1.78657615210.4%
 
-1.75848746310.4%
 
ValueCountFrequency (%) 
2.30313491810.4%
 
2.07148528110.4%
 
2.0514748110.4%
 
2.03420758210.4%
 
1.90777516410.4%
 
1.89014375210.4%
 
1.87375545510.4%
 
1.84666621710.4%
 
1.8293929110.4%
 
1.72319662610.4%
 

oz2
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.695007085800171e-09
Minimum-1.733388066291809
Maximum1.6868163347244265
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:56:59.784803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.733388066
5-th percentile-1.581239092
Q1-0.7483351231
median-0.01530450257
Q30.8115198463
95-th percentile1.560419756
Maximum1.686816335
Range3.420204401
Interquartile range (IQR)1.559854969

Descriptive statistics

Standard deviation0.9999999951
Coefficient of variation (CV)-589968032.3
Kurtosis-1.142415619
Mean-1.695007086e-09
Median Absolute Deviation (MAD)0.8003616664
Skewness-0.002024361984
Sum-4.237517715e-07
Variance0.9999999903
2020-08-25T00:56:59.888738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.24999737710.4%
 
1.68681633510.4%
 
0.704770386210.4%
 
-0.13944920910.4%
 
-1.59801638110.4%
 
-1.13121449910.4%
 
0.804511547110.4%
 
0.181652426710.4%
 
0.587522566310.4%
 
1.35190892210.4%
 
1.19192564510.4%
 
-1.24643385410.4%
 
0.975844144810.4%
 
1.65951466610.4%
 
-0.571941912210.4%
 
0.760080218310.4%
 
-0.656695663910.4%
 
-0.812657713910.4%
 
-1.65691816810.4%
 
-0.668608009810.4%
 
0.760631859310.4%
 
-0.818994224110.4%
 
0.95922362810.4%
 
-1.42800915210.4%
 
-1.52298545810.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.73338806610.4%
 
-1.72949194910.4%
 
-1.71387195610.4%
 
-1.71273958710.4%
 
-1.69937455710.4%
 
-1.65691816810.4%
 
-1.65593135410.4%
 
-1.64639127310.4%
 
-1.61372995410.4%
 
-1.60782206110.4%
 
ValueCountFrequency (%) 
1.68681633510.4%
 
1.67880022510.4%
 
1.67759847610.4%
 
1.67238354710.4%
 
1.66570472710.4%
 
1.66420507410.4%
 
1.65951466610.4%
 
1.6444226510.4%
 
1.62742435910.4%
 
1.59462845310.4%
 

oz3
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.156943082809448e-09
Minimum-1.8650447130203247
Maximum3.1398279666900635
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:57:00.005874image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.865044713
5-th percentile-1.342448711
Q1-0.7469610125
median-0.1700332835
Q30.5734898001
95-th percentile2.017714238
Maximum3.139827967
Range5.00487268
Interquartile range (IQR)1.320450813

Descriptive statistics

Standard deviation0.9999999901
Coefficient of variation (CV)-463619090.4
Kurtosis0.2953295037
Mean-2.156943083e-09
Median Absolute Deviation (MAD)0.6552530676
Skewness0.7980627999
Sum-5.392357707e-07
Variance0.9999999801
2020-08-25T00:57:00.116212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.02538824110.4%
 
-0.053630407910.4%
 
0.482030361910.4%
 
-0.829535782310.4%
 
-0.631293356410.4%
 
-1.05115997810.4%
 
0.372408002610.4%
 
-0.248703867210.4%
 
-0.502626478710.4%
 
-0.0565375834710.4%
 
1.54626226410.4%
 
-0.153975576210.4%
 
2.01838779410.4%
 
-0.246870636910.4%
 
0.0699073523310.4%
 
-0.36079382910.4%
 
-1.28936314610.4%
 
1.58674454710.4%
 
-1.00779128110.4%
 
-0.162517875410.4%
 
1.66341590910.4%
 
-0.204629555310.4%
 
-0.104533821310.4%
 
1.97901594610.4%
 
0.761696994310.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.86504471310.4%
 
-1.6488747610.4%
 
-1.55841684310.4%
 
-1.55257546910.4%
 
-1.54562413710.4%
 
-1.53509509610.4%
 
-1.47661805210.4%
 
-1.43307995810.4%
 
-1.38095891510.4%
 
-1.35761916610.4%
 
ValueCountFrequency (%) 
3.13982796710.4%
 
3.08296918910.4%
 
2.81638550810.4%
 
2.55248951910.4%
 
2.44413328210.4%
 
2.41765570610.4%
 
2.40914225610.4%
 
2.40575408910.4%
 
2.16096210510.4%
 
2.12120604510.4%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.9259750807520958e-09
Minimum-1.8222055435180664
Maximum1.5664935111999512
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:57:00.237479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.822205544
5-th percentile-1.64236654
Q1-0.7542671561
median-0.003542026505
Q30.8663032055
95-th percentile1.430964136
Maximum1.566493511
Range3.388699055
Interquartile range (IQR)1.620570362

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-519217517.9
Kurtosis-1.169004365
Mean-1.925975081e-09
Median Absolute Deviation (MAD)0.8366000354
Skewness-0.147755665
Sum-4.814937702e-07
Variance1.000000002
2020-08-25T00:57:00.350880image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.06836295110.4%
 
0.0901691243110.4%
 
-0.532148063210.4%
 
1.050185810.4%
 
1.2943180810.4%
 
1.46814274810.4%
 
-0.512234330210.4%
 
0.866391003110.4%
 
0.0456040911410.4%
 
-0.856617629510.4%
 
1.23471677310.4%
 
0.902173936410.4%
 
-1.27963149510.4%
 
-1.77278876310.4%
 
1.1344708210.4%
 
0.83173304810.4%
 
-1.47590494210.4%
 
1.22395098210.4%
 
0.771677076810.4%
 
0.49153348810.4%
 
1.48273265410.4%
 
1.2366259110.4%
 
0.492017269110.4%
 
-1.12310421510.4%
 
-0.537743270410.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.82220554410.4%
 
-1.81053197410.4%
 
-1.80610096510.4%
 
-1.78264749110.4%
 
-1.77278876310.4%
 
-1.76910674610.4%
 
-1.75891900110.4%
 
-1.70954561210.4%
 
-1.69531917610.4%
 
-1.68456685510.4%
 
ValueCountFrequency (%) 
1.56649351110.4%
 
1.54876995110.4%
 
1.50695407410.4%
 
1.50693273510.4%
 
1.50210595110.4%
 
1.49396932110.4%
 
1.49113285510.4%
 
1.48852145710.4%
 
1.48273265410.4%
 
1.46814274810.4%
 

oz5
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7816200852394104e-09
Minimum-1.715514063835144
Maximum1.6187453269958496
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:57:00.475526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.715514064
5-th percentile-1.558108491
Q1-0.8758047968
median0.02653695829
Q30.8979841918
95-th percentile1.529741448
Maximum1.618745327
Range3.334259391
Interquartile range (IQR)1.773788989

Descriptive statistics

Standard deviation1.000000003
Coefficient of variation (CV)561286893.7
Kurtosis-1.234270435
Mean1.781620085e-09
Median Absolute Deviation (MAD)0.8920594584
Skewness-0.06439161278
Sum4.454050213e-07
Variance1.000000007
2020-08-25T00:57:00.583090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.179321259310.4%
 
-0.839516699310.4%
 
-0.215745046710.4%
 
-1.40955090510.4%
 
0.90350401410.4%
 
1.57722878510.4%
 
1.6068060410.4%
 
-0.75262039910.4%
 
1.52729082110.4%
 
1.10289144510.4%
 
-0.280603319410.4%
 
0.58659511810.4%
 
1.55579447710.4%
 
1.59896445310.4%
 
1.50716328610.4%
 
-0.819008052310.4%
 
0.630010306810.4%
 
1.05076110410.4%
 
-0.50357246410.4%
 
-0.333328902710.4%
 
0.291336268210.4%
 
1.08428788210.4%
 
-0.300610184710.4%
 
1.18582677810.4%
 
1.49050486110.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.71551406410.4%
 
-1.7107218510.4%
 
-1.69712543510.4%
 
-1.69604051110.4%
 
-1.69340002510.4%
 
-1.69203877410.4%
 
-1.66037380710.4%
 
-1.6603717810.4%
 
-1.62394642810.4%
 
-1.61663842210.4%
 
ValueCountFrequency (%) 
1.61874532710.4%
 
1.60776388610.4%
 
1.6068060410.4%
 
1.59896445310.4%
 
1.57722878510.4%
 
1.57722425510.4%
 
1.57562375110.4%
 
1.56568372210.4%
 
1.55579447710.4%
 
1.54671394810.4%
 

oz6
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.96515154838562e-10
Minimum-1.6875637769699097
Maximum1.6541893482208252
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:57:00.708530image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.687563777
5-th percentile-1.522901517
Q1-0.9110902995
median-0.05172674544
Q30.9014867097
95-th percentile1.490943736
Maximum1.654189348
Range3.341753125
Interquartile range (IQR)1.812577009

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-1003497033
Kurtosis-1.29473202
Mean-9.965151548e-10
Median Absolute Deviation (MAD)0.8965339065
Skewness0.01085372857
Sum-2.491287887e-07
Variance1.000000002
2020-08-25T00:57:00.814128image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.00721739279110.4%
 
0.888826191410.4%
 
1.14683449310.4%
 
1.42710864510.4%
 
0.909293115110.4%
 
-0.711597621410.4%
 
-1.02963697910.4%
 
0.324137896310.4%
 
1.02154886710.4%
 
-0.866376042410.4%
 
0.604168057410.4%
 
-1.55403733310.4%
 
-1.25129723510.4%
 
-0.73763132110.4%
 
0.0360663421510.4%
 
-0.860015988310.4%
 
1.31629908110.4%
 
1.44268441210.4%
 
1.41436135810.4%
 
-0.622706472910.4%
 
1.15654063210.4%
 
1.15848910810.4%
 
-1.48172783910.4%
 
1.39676642410.4%
 
1.37918508110.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.68756377710.4%
 
-1.66277849710.4%
 
-1.65970325510.4%
 
-1.65645837810.4%
 
-1.63580203110.4%
 
-1.62341308610.4%
 
-1.60669374510.4%
 
-1.57363927410.4%
 
-1.55475628410.4%
 
-1.55403733310.4%
 
ValueCountFrequency (%) 
1.65418934810.4%
 
1.65296506910.4%
 
1.65022683110.4%
 
1.64548766610.4%
 
1.64439332510.4%
 
1.61050295810.4%
 
1.59573316610.4%
 
1.58407378210.4%
 
1.57978820810.4%
 
1.55439209910.4%
 

oz7
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8812716007232666e-09
Minimum-1.6973079442977903
Maximum1.6855038404464722
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:57:00.932263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.697307944
5-th percentile-1.542426378
Q1-0.8473253846
median-0.05288272351
Q30.9630089998
95-th percentile1.511002219
Maximum1.68550384
Range3.382811785
Interquartile range (IQR)1.810334384

Descriptive statistics

Standard deviation1.000000003
Coefficient of variation (CV)531555359.9
Kurtosis-1.254310053
Mean1.881271601e-09
Median Absolute Deviation (MAD)0.9238316789
Skewness0.02594767229
Sum4.703179002e-07
Variance1.000000006
2020-08-25T00:57:01.037805image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.27876436710.4%
 
-1.29032993310.4%
 
1.08823585510.4%
 
-1.02670824510.4%
 
-1.55990958210.4%
 
1.11557149910.4%
 
1.46712613110.4%
 
0.912436664110.4%
 
-1.29524183310.4%
 
1.31316912210.4%
 
0.0573826357710.4%
 
-1.50554239710.4%
 
0.925930023210.4%
 
0.353345572910.4%
 
-1.41631114510.4%
 
-0.251590102910.4%
 
0.425967425110.4%
 
1.41630291910.4%
 
1.0403183710.4%
 
-1.59686219710.4%
 
-0.0999921560310.4%
 
1.34987556910.4%
 
1.42799735110.4%
 
-0.358951419610.4%
 
-1.00362408210.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.69730794410.4%
 
-1.69724726710.4%
 
-1.67858600610.4%
 
-1.67257189810.4%
 
-1.65981566910.4%
 
-1.65227007910.4%
 
-1.62651646110.4%
 
-1.59686219710.4%
 
-1.59291124310.4%
 
-1.58503007910.4%
 
ValueCountFrequency (%) 
1.6855038410.4%
 
1.66823244110.4%
 
1.64670491210.4%
 
1.61197423910.4%
 
1.60525834610.4%
 
1.59276294710.4%
 
1.54728150410.4%
 
1.54681754110.4%
 
1.5294212110.4%
 
1.52778124810.4%
 

oz8
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6226043701171873e-09
Minimum-1.6782217025756836
Maximum1.7388352155685425
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:57:01.162079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.678221703
5-th percentile-1.568145102
Q1-0.8539417535
median-0.04403080232
Q30.8030073792
95-th percentile1.605234432
Maximum1.738835216
Range3.417056918
Interquartile range (IQR)1.656949133

Descriptive statistics

Standard deviation1.000000004
Coefficient of variation (CV)381300365.1
Kurtosis-1.123014418
Mean2.62260437e-09
Median Absolute Deviation (MAD)0.8315048683
Skewness0.02778830917
Sum6.556510925e-07
Variance1.000000008
2020-08-25T00:57:01.270322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.390184104410.4%
 
-0.553884923510.4%
 
1.05744171110.4%
 
-1.28066480210.4%
 
-1.36171400510.4%
 
1.49745547810.4%
 
-1.66486024910.4%
 
0.486911475710.4%
 
-0.0248929746410.4%
 
-1.3656059510.4%
 
1.02600061910.4%
 
-1.67311894910.4%
 
-1.67320716410.4%
 
1.72007799110.4%
 
-0.818046450610.4%
 
-1.20279276410.4%
 
1.13993084410.4%
 
0.409980177910.4%
 
-1.45184218910.4%
 
1.60873675310.4%
 
-1.55178654210.4%
 
0.467694163310.4%
 
-0.135903164710.4%
 
1.1594898710.4%
 
-1.06377935410.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.67822170310.4%
 
-1.67528295510.4%
 
-1.67320716410.4%
 
-1.67311894910.4%
 
-1.66486024910.4%
 
-1.66464567210.4%
 
-1.63274550410.4%
 
-1.60711908310.4%
 
-1.59365308310.4%
 
-1.59261798910.4%
 
ValueCountFrequency (%) 
1.73883521610.4%
 
1.73440408710.4%
 
1.72675204310.4%
 
1.72007799110.4%
 
1.70951819410.4%
 
1.69800353110.4%
 
1.68329536910.4%
 
1.67260682610.4%
 
1.66822004310.4%
 
1.65238690410.4%
 

oz9
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.3958425521850584e-10
Minimum-1.5796327590942385
Maximum1.7653772830963137
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:57:01.387285image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.579632759
5-th percentile-1.428347492
Q1-0.8519166559
median-0.08416427672
Q30.8702519983
95-th percentile1.529598165
Maximum1.765377283
Range3.345010042
Interquartile range (IQR)1.722168654

Descriptive statistics

Standard deviation0.9999999992
Coefficient of variation (CV)-2274876744
Kurtosis-1.278969589
Mean-4.395842552e-10
Median Absolute Deviation (MAD)0.9263144881
Skewness0.06763332901
Sum-1.098960638e-07
Variance0.9999999984
2020-08-25T00:57:01.498022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.53027164910.4%
 
0.690047144910.4%
 
0.861514747110.4%
 
-0.111351020610.4%
 
-0.0131101692110.4%
 
-1.35740530510.4%
 
-1.31779468110.4%
 
1.39767694510.4%
 
-0.249677598510.4%
 
-1.5657805210.4%
 
0.806811332710.4%
 
-0.22436679910.4%
 
1.53720748410.4%
 
-0.691480219410.4%
 
-0.477621883210.4%
 
-0.715491414110.4%
 
-1.04583311110.4%
 
-0.276446372310.4%
 
-0.486249327710.4%
 
-0.506992578510.4%
 
-0.460320740910.4%
 
1.7170840510.4%
 
0.877927064910.4%
 
0.710098683810.4%
 
1.76492214210.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.57963275910.4%
 
-1.5657805210.4%
 
-1.55947864110.4%
 
-1.55713617810.4%
 
-1.54706549610.4%
 
-1.53857719910.4%
 
-1.53027164910.4%
 
-1.51612210310.4%
 
-1.51076364510.4%
 
-1.47660589210.4%
 
ValueCountFrequency (%) 
1.76537728310.4%
 
1.76492214210.4%
 
1.76357650810.4%
 
1.75668585310.4%
 
1.7170840510.4%
 
1.70942926410.4%
 
1.69665312810.4%
 
1.6708904510.4%
 
1.64687538110.4%
 
1.64161539110.4%
 

oz10
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.3271650912734e-10
Minimum-1.6876388788223269
Maximum1.6847131252288818
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:57:01.783696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.687638879
5-th percentile-1.505480504
Q1-0.9188119173
median-0.0001766304485
Q30.8879930228
95-th percentile1.550617951
Maximum1.684713125
Range3.372352004
Interquartile range (IQR)1.80680494

Descriptive statistics

Standard deviation0.9999999982
Coefficient of variation (CV)-1877171030
Kurtosis-1.231338695
Mean-5.327165091e-10
Median Absolute Deviation (MAD)0.9117643237
Skewness0.01158433525
Sum-1.331791273e-07
Variance0.9999999963
2020-08-25T00:57:01.887740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.910608470410.4%
 
-1.25424587710.4%
 
-0.897651851210.4%
 
-1.0120911610.4%
 
0.0277005005610.4%
 
0.140915334210.4%
 
0.923524081710.4%
 
0.206588730210.4%
 
1.04242050610.4%
 
-0.61932003510.4%
 
0.208785012410.4%
 
1.32262003410.4%
 
-0.747246205810.4%
 
-1.045551310.4%
 
-0.446995407310.4%
 
0.486900657410.4%
 
-1.26394164610.4%
 
-1.24642753610.4%
 
-0.659346103710.4%
 
-0.410887330810.4%
 
-0.319660037810.4%
 
0.101276919210.4%
 
0.254560828210.4%
 
0.0900460928710.4%
 
0.579740345510.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.68763887910.4%
 
-1.65354418810.4%
 
-1.62832784710.4%
 
-1.6090177310.4%
 
-1.60765695610.4%
 
-1.58341968110.4%
 
-1.57065224610.4%
 
-1.56958615810.4%
 
-1.56142747410.4%
 
-1.54924905310.4%
 
ValueCountFrequency (%) 
1.68471312510.4%
 
1.65682995310.4%
 
1.64354276710.4%
 
1.61785352210.4%
 
1.61375498810.4%
 
1.60938334510.4%
 
1.60784435310.4%
 
1.60760545710.4%
 
1.60258829610.4%
 
1.60120677910.4%
 

target
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.601874828338623e-10
Minimum-2.3651328086853027
Maximum1.9151029586791992
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:57:02.004420image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.365132809
5-th percentile-1.873409086
Q1-0.7968685776
median0.2978260964
Q30.7520932257
95-th percentile1.259691936
Maximum1.915102959
Range4.280235767
Interquartile range (IQR)1.548961803

Descriptive statistics

Standard deviation1.000000004
Coefficient of variation (CV)-6242685050
Kurtosis-0.6771083264
Mean-1.601874828e-10
Median Absolute Deviation (MAD)0.5817624778
Skewness-0.645545618
Sum-4.004687071e-08
Variance1.000000009
2020-08-25T00:57:02.105502image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.167480304810.4%
 
-1.51494705710.4%
 
0.714476704610.4%
 
-1.7864769710.4%
 
-0.476647973110.4%
 
0.937178790610.4%
 
0.49764192110.4%
 
0.437041252910.4%
 
-1.53106582210.4%
 
1.00813126610.4%
 
0.17252509310.4%
 
-2.14125728610.4%
 
0.247110649910.4%
 
-1.26007354310.4%
 
0.560884416110.4%
 
0.55523884310.4%
 
1.23559415310.4%
 
0.392214804910.4%
 
-0.280503541210.4%
 
0.907854378210.4%
 
-1.67117607610.4%
 
0.440742820510.4%
 
1.03309905510.4%
 
0.914683520810.4%
 
-0.36355593810.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-2.36513280910.4%
 
-2.23282980910.4%
 
-2.22536110910.4%
 
-2.14125728610.4%
 
-2.10327506110.4%
 
-1.96952652910.4%
 
-1.94539046310.4%
 
-1.93300032610.4%
 
-1.92884874310.4%
 
-1.910049210.4%
 
ValueCountFrequency (%) 
1.91510295910.4%
 
1.73285961210.4%
 
1.65892350710.4%
 
1.51031863710.4%
 
1.41335105910.4%
 
1.39653396610.4%
 
1.39524209510.4%
 
1.38970744610.4%
 
1.38362848810.4%
 
1.30804884410.4%
 

Interactions

2020-08-25T00:56:43.243013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:43.353002image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:43.468091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:43.579191image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:43.696924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:43.829529image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:43.949483image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:44.069639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:44.187430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:44.305470image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:44.425235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:44.533668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:44.651831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:44.777745image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:44.901643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:45.028728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:45.156197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:45.283067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:45.413232image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:45.540422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:45.681661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:45.814487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:45.938616image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:46.052196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:46.172277image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:46.455329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:46.579547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:46.700938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:46.822309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:46.946988image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:47.066902image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:47.187363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:47.307650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:47.424571image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:47.548024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:47.676354image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:47.804817image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:47.938405image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:48.063719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:48.189557image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:48.316384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:48.445804image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:48.567874image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:48.707525image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:48.829439image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:48.949503image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:49.077905image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:49.197503image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:49.321721image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:49.449310image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:49.574030image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:49.698792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:49.821683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:49.953897image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:50.077810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:50.359996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:50.477126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:50.601468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:50.720614image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:50.844589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:50.974885image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:51.100444image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:51.225382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:51.351835image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:51.479309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:51.605086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:51.721719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:51.844574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:51.975305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:52.098561image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:52.226049image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:52.359955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:52.489155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:52.611553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:52.733381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:52.859318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:52.988100image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:53.108108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:53.229085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:53.358026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:53.480850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:53.615704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:53.743858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:53.872612image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:54.007247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:54.304974image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:54.430507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:54.559434image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:54.685253image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:54.805420image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:54.939332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:55.079898image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:55.220604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:55.349485image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:55.482413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:55.619694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:55.746531image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:55.872591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:56.003245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:56.122617image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:56.256837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:56.384605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:56.512020image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:56.641245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:56.843214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:56.977795image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:57.106471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:57.233727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:57.360998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:57.488743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:57.618198image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:57.729163image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:57.845010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:57.956416image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:58.074098image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:58.360575image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:58.477255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:58.592368image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:58.706182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:58.824374image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:58.942899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:57:02.228260image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:57:02.451077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:57:02.676297image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:57:02.899215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:56:59.163859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:56:59.433644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
00.7006350.4416021.4940981.360279-1.313559-0.570020-1.141491-0.1991510.726280-1.358638-1.202063
1-0.138027-0.165849-0.699045-0.6263370.1022830.287011-1.195182-1.3657200.8615151.0409790.909621
20.8856201.128012-0.990696-1.8061011.3033651.4383481.387324-1.551787-0.0373851.239041-1.131109
31.8737551.664205-1.025388-1.566213-1.333236-1.5267440.8916580.4676940.706546-1.2456330.067172
40.239419-0.243991-0.728041-0.532148-1.6934001.328735-0.053328-0.4007461.6468751.5047950.150110
5-0.2402460.0762430.026623-0.3793340.8649891.4906621.0385310.390184-0.133911-0.3881330.367832
6-1.085530-1.149106-0.182410-0.4900650.862565-0.563823-1.040875-0.1909830.276588-1.4375080.075018
7-1.137630-0.659949-1.357350-1.2844311.301799-1.2128091.246786-1.091367-1.4101070.1012771.013180
80.6935281.0163172.4176561.317228-0.7739660.4670261.592763-1.4092560.2769111.196883-1.049799
9-1.155839-1.656918-1.1366380.831733-1.697125-0.8647591.529421-1.3617140.1417130.4587250.207977

Last rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
2400.8809191.5824180.777226-0.0611140.023097-0.4386581.021032-1.1846250.3144771.533856-0.344296
241-0.0513770.1547230.323770-0.035699-0.1679700.0360661.1624721.263674-1.317795-0.1244020.147877
242-0.2568040.053894-0.829536-1.1231040.294027-1.443161-0.123212-1.3803820.9849490.7109170.703388
2430.8514810.5908720.092162-0.4474481.096134-0.9479670.7351851.738835-1.054433-0.975409-1.628497
2440.010233-0.837488-0.357196-0.1191320.9509200.7837570.382320-0.131413-0.611139-0.1130530.863235
245-0.158230-0.716542-0.579113-0.4584211.0871471.076529-0.019662-0.986233-0.2007830.5258591.005669
246-0.0506670.6913281.3637160.902174-1.422609-0.415201-1.0236020.3631521.320138-0.033236-0.834156
247-1.375244-1.299815-1.1243770.959765-0.3852501.396766-1.2903300.4099800.259944-1.0174740.865233
2480.3020960.7703070.5711800.6811380.2387091.158489-0.251590-0.566132-0.0903171.490682-1.287641
249-0.858230-0.725785-0.093375-0.4914051.196735-0.277361-0.837435-0.144351-0.0505391.0406420.638128